Using machine learning algorithms based on patient admission laboratory parameters to predict adverse outcomes in COVID-19 patients.

Amidst the global COVID-19 pandemic, the urgent need for timely and precise patient prognosis assessment underscores the significance of leveraging machine learning techniques. In this study, we present a novel predictive model centered on routine clinical laboratory test data to swiftly forecast patient survival outcomes upon admission. Our model integrates feature selection algorithms and binary classification algorithms, optimizing algorithmic selection through meticulous parameter control. Notably, we developed an algorithm coupling Lasso and SVM methodologies, achieving a remarkable area under the ROC curve of 0.9277 with the use of merely 8 clinical laboratory parameters collected upon admission. Our primary contribution lies in the utilization of straightforward laboratory parameters for prognostication, circumventing data processing intricacies, and furnishing clinicians with an expeditious and precise prognostic assessment tool.

Comparative Study on the Effectiveness of Three Inoculation Methods for Valsa sordida in Populus alba var. pyramidalis.

A key step in the study of tree pathology is the identification of an appropriate method for inoculating pathogens of diseases in branches and trunks. Pathogens of diseases in branches and trunks are commonly inoculated through punching, burning, and toothpick inoculation. However, there is a lack of comparative analyses of the inoculation outcomes of these three methods. In this work, six-year-old P. alba var. pyramidalis were inoculated with V. sordida using punching, burning, and toothpick techniques to investigate the differences in the effectiveness of these inoculation methods. Results reveal that the incidence rate was 93.55% in the toothpick inoculation group, significantly higher than the 80.65% in the burning inoculation group (chi-square, n = 90, p = 0.007), while punching inoculation exhibited significant pathological responses in the early stages, with spontaneous healing in the later stage. Additionally, toothpick inoculation was more efficient in inducing Valsa canker when inoculating the pathogen at the bottom of the tree, with lower intra- and inter-row spacing (stand density) providing better outcomes than higher intra- and inter-row spacing. The results of this study demonstrate that toothpick inoculation is an optimal option for studying the artificial inoculation of V. sordida in six-year-old P. alba var. pyramidalis, providing technical support for research on poplar diseases and offering a theoretical basis for the inoculation of other diseases in the branch and trunk.

Reconfigurable aqueous 3D printing with adaptive dual locks.

Using aqueous two-phase systems (ATPSs) for three-dimensional (3D) printed complex structures has attracted considerable attention in the field of biomedicine. In this study, we present an unusual approach to constructing reconfigurable 3D printed structures within an aqueous environment. Inspired by biological systems, we introduce both specific and nonspecific interactions to anchor functionalized nanoparticles to the water-water interface, thereby imparting adaptive dual locks of structural integrity and permeability to the 3D printed liquid structures. Using state-of-the-art in situ liquid-liquid interfacial atomic force microscopy imaging, we successfully demonstrate various morphologies of interfacial films formed at the ATPS interface. In addition, by incorporating d-glucose or sodium alginate into the systems, the dual locks can be easily manipulated. Our study paves a pathway for 3D printing multiresponsive all-aqueous systems with controllable structures and permeability, showing promising implications for the development of smart drug delivery systems and in vivo reactions.

Guben Qingfei decoction attenuates LPS-induced acute lung injury by modulating the TLR4/NF-κB and Keap1/Nrf2 signaling pathways.

ETHNOPHARMACOLOGICAL RELEVANCE: Acute lung injury (ALI) is a life-threatening and widespread disease, with exceptionally high morbidity and mortality rates. Unfortunately, effective drugs for ALI treatment are currently lacking. Guben Qingfei decoction (GBQF) is a Chinese herbal compound known for its efficacy in treating viral pneumonia, yet the precise underlying mechanisms remain unknown. AIM OF THE STUDY: This study aimed to validate the mitigating effect of GBQF on ALI and to further investigate its mechanism. MATERIALS AND METHODS: An ALI mice model was established by infusing LPS into the endotracheal tube. The effects of GBQF on ALI were investigated by measuring lung W/D; MPO; BALF total protein concentration; total number of cells; TNF-α, IL-1ß, and IL-6 levels; pathological changes in lung tissue, and oxidation products. Immunohistochemistry and Western Blotting were performed to verify the underlying mechanisms. MH-S and BEAS-2B cells were induced by LPS, and the effects of GBQF were confirmed by RT-PCR and immunofluorescence. RESULTS: GBQF significantly reduced LPS-induced ALI in mice, improved lung inflammation, reduced the production of oxidative products, increased the activity of antioxidant enzymes, and reduced the degree of lung tissue damage. GBQF prevents MH-S cells from releasing inflammatory factors and reduces oxidative damage to BEAS-2B cells. In vivo studies have delved deeper into the mechanism of action of GBQF, revealing its correlation with the TLR4/NF-κB and Keap1/Nrf2 pathways. CONCLUSIONS: Our study demonstrates that GBQF is an effective treatment for ALI, providing a new perspective on medication development for ALI treatment.

Integrated transcriptome and microRNA sequencing analyses reveal gene responses in poplar leaves infected by the novel pathogen bean common mosaic virus (BCMV).

Recently, a novel poplar mosaic disease caused by bean common mosaic virus (BCMV) was investigated in Populus alba var. pyramidalis in China. Symptom characteristics, physiological performance of the host, histopathology, genome sequences and vectors, and gene regulation at the transcriptional and posttranscriptional levels were analyzed and RT-qPCR (quantitative reverse transcription PCR) validation of expression was performed in our experiments. In this work, the mechanisms by which the BCMV pathogen impacts physiological performance and the molecular mechanisms of the poplar response to viral infection were reported. The results showed that BCMV infection decreased the chlorophyll content, inhibited the net photosynthesis rate (Pn) and stomatal conductance (Gs), and significantly changed chlorophyll fluorescence parameters in diseased leaves. Transcriptome analysis revealed that the expression of the majority of DEGs (differentially expressed genes) involved in the flavonoid biosynthesis pathway was promoted, but the expression of all or almost all DEGs associated with photosynthesis-antenna proteins and the photosynthesis pathway was inhibited in poplar leaves, suggesting that BCMV infection increased the accumulation of flavonoids but decreased photosynthesis in hosts. Gene set enrichment analysis (GSEA) illustrated that viral infection promoted the expression of genes involved in the defense response or plant-pathogen interaction. MicroRNA-seq analysis illustrated that 10 miRNA families were upregulated while 6 families were downregulated in diseased poplar leaves; moreover, miR156, the largest family with the most miRNA members and target genes, was only differentially upregulated in long-period disease (LD) poplar leaves. Integrated transcriptome and miRNA-seq analyses revealed 29 and 145 candidate miRNA-target gene pairs; however, only 17 and 76 pairs, accounting for 2.2% and 3.2% of all DEGs, were authentically negatively regulated in short-period disease (SD) and LD leaves, respectively. Interestingly, 4 miR156/SPL (squamosa promoter-binding-like protein) miRNA-target gene pairs were identified in LD leaves: the miR156 molecules were upregulated, but SPL genes were downregulated. In conclusion, BCMV infection significantly changed transcriptional and posttranscriptional gene expression in poplar leaves, inhibited photosynthesis, increased the accumulation of flavonoids, induced systematic mosaic symptoms, and decreased physiological performance in diseased poplar leaves. This study elucidated the fine-tuned regulation of poplar gene expression by BCMV; moreover, the results also suggested that miR156/SPL modules played important roles in the virus response and development of viral systematic symptoms in plant virus disease.

Using Aggregation to Chaperone Nanoparticles Across Fluid Interfaces.

Nanoparticles (NPs) transfer is usually induced by adding ligands to modify NP surfaces, but aggregation of NPs oftentimes hampers the transfer. Here, we show that aggregation during NP phase transfer does not necessarily result in transfer failure. Using a model system comprising gold NPs and amphiphilic polymers, we demonstrate an unusual mechanism by which NPs can undergo phase transfer from the aqueous phase to the organic phase via a single-aggregation-single pathway. Our discovery challenges the conventional idea that aggregation inhibits NP transfer and provides an unexpected pathway for transferring larger-sized NPs (>20 nm). The charged amphiphilic polymers effectively act as chaperons for the NP transfer and offer a unique way to manipulate the dispersion and distribution of NPs in two immiscible liquids. Moreover, by intentionally jamming the NP-polymer assembly at the liquid/liquid interface, the transfer process can be inhibited.

Exploring the Mechanism of Bergamot Essential Oil against Asthma Based on Network Pharmacology and Experimental Verification.

Asthma is a chronic respiratory disease. Bergamot essential oil (BEO) is extracted from the bergamot peel, which is widely used as a medicinal and food plant in China. Modern pharmacological studies have confirmed that BEO has anti-inflammatory properties, suggesting potential in treating asthma. First, the main active ingredients of BEO were detected and analyzed by gas chromatography-mass spectrometry (GC-MS). Network pharmacology methods were used to explore the possible core targets and main pathways of BEO in asthma treatment. Then ovalbumin (OVA)-induced in vivo and lipopolysaccharide (LPS)-induced in vitro models were established to investigate the antiasthmatic effects of BEO. BEO showed a good antiasthmatic effect by improving lung inflammation and inhibiting collagen deposition. Then, enzyme-linked immunosorbent assay (ELISA) and quantitative real-time polymerase chain reaction (qPCR) were used to explore the possible mechanism of BEO in asthma treatment. Furthermore, experimental verification showed that BEO could suppress the release of inflammatory factors in vitro and inhibit the activation of MAPK and JAK-STAT signaling pathways. This study demonstrated the anti-inflammatory effects of BEO against asthma. Moreover, it supplies a theoretical basis for the clinical application of BEO.

[Introduction of legislation of disciplinary system for acupuncturists in various states in the United States: taking Ohio and other 11 states as examples].

With 12 representative states such as Ohio and Illinois as examples, the disciplinary system for acupuncturists in the United States is introduced. The disciplinary system mainly covers several aspects such as the subject of the implementation, the types of illegal acts, the form of disciplinary responsibility, and the hearing procedure. The acupuncture industry association has the responsibility of disciplinary action in most states. Corresponding disciplinary measures will be taken depending on the illegal activities of acupuncturists according to the types of illegal practice specified by each state. The hearing procedure provides procedural guarantee for the subsequent rights protection activities of acupuncturists.

Shape-Reconfigurable Ferrofluids.

Ferrofluids (FFs) can adapt their shape to a magnetic field. However, they cannot maintain their shape when the magnetic field is removed. Here, with a magneto-responsive and reconfigurable interfacial self-assembly (MRRIS) process, we show that FFs can be structured by a magnetic field and maintain their shape, like solids, after removing the magnetic field. The competing self-assembly of magnetic and nonmagnetic nanoparticles at the liquid interface endow FFs with both reconfigurability and structural stability. By manipulating the external magnetic field, we show that it is possible to "write" and "erase" the shape of the FFs remotely and repeatedly. To gain an in-depth understanding of the effect of MRRIS on the structure of FFs, we systematically study the shape variation of these liquids under both the static and dynamic magnetic fields. Our study provides a simple yet novel way of manipulating FFs and opens opportunities for the fabrication of all-liquid devices.

Not out of the woods yet: Signatures of the prolonged negative genetic consequences of a population bottleneck in a rapidly re-expanding wader, the black-faced spoonbill Platalea minor.

The long-term persistence of a population which has suffered a bottleneck partly depends on how historical demographic dynamics impacted its genetic diversity and the accumulation of deleterious mutations. Here we provide genomic evidence for the genetic effect of a recent population bottleneck in the endangered black-faced spoonbill (Platalea minor) after its rapid population recovery. Our data suggest that the bird's effective population size, Ne , had been relatively stable (7500-9000) since 22,000 years ago; however, a recent brief yet severe bottleneck (Ne  = 20) which we here estimated to occur around the 1940s wiped out >99% of its historical Ne in roughly three generations. Despite a >15-fold population recovery since 1988, we found that black-faced spoonbill population has higher levels of inbreeding (7.4 times more runs of homozygosity) than its sister species, the royal spoonbill (P. regia), which is not thought to have undergone a marked population contraction. Although the two spoonbills have similar levels of genome-wide genetic diversity, our results suggest that selection on more genes was relaxed in the black-faced spoonbill; moreover individual black-faced spoonbills carry more putatively deleterious mutations (Grantham's score > 50), and may therefore express more deleterious phenotypic effects than royal spoonbills. Here we demonstrate the value of using genomic indices to monitor levels of genetic erosion, inbreeding and mutation load in species with conservation concerns. To mitigate the prolonged negative genetic effect of a population bottleneck, we recommend that all possible measures should be employed to maintain population growth of a threatened species.

Morphological variations in a widely distributed Eastern Asian passerine cannot be consistently explained by ecogeographic rules.

Ecogeographic rules that describe quantitative relationships between morphologies and climate might help us predict how morphometrics of animals was shaped by local temperature or humidity. Although the ecogeographic rules had been widely tested in animals of Europe and North America, they had not been fully validated for species in regions that are less studied. Here, we investigate the morphometric variation of a widely distributed East Asian passerine, the vinous-throated parrotbill (Sinosuthora webbiana), to test whether its morphological variation conforms to the prediction of Bergmann's rule, Allen's rules, and Gloger's rule. We at first described the climatic niche of S. webbiana from occurrence records (n = 7838) and specimen records (n = 290). The results of analysis of covariance (ANCOVA) suggested that the plumage coloration of these parrotbills was darker in wetter/warmer environments following Gloger's rule. However, their appendage size (culmen length, beak volume, tarsi length) was larger in colder environments, the opposite of the predictions of Allen's rule. Similarly, their body size (wing length) was larger in warmer environments, the opposite of the predictions of Bergmann's rule. Such disconformity to both Bergmann's rule and Allen's rule suggests that the evolution of morphological variations is likely governed by multiple selection forces rather than dominated by thermoregulation. Our results suggest that these ecogeographic rules should be validated prior to forecasting biological responses to climate change especially for species in less-studied regions.

Multi-stroke anti-hysteresis algorithm based on inverse model.

The control strategy of multi-stroke repetitive driving is often required in the micro-nano-system. However, the current research only focuses on the modeling algorithm of a single stroke; thus, it is difficult to realize the hysteresis compensation at any stroke of the piezoelectric actuator. This paper proposes a multi-stroke compensation algorithm combined with the inverse model: through the analysis of the hysteresis model, the rising trajectory with better linearity is defined as the target line to find a mathematical expression with fewer parameters to describe the falling trajectory, then to find the relationship between maximum stroke voltage and the above parameters to establish a mathematical model that can describe any stroke, and finally, to verify that the above model applies to any unknown stroke, thereby realizing multi-stroke compensation. The experimental results show that the hysteresis corrected rate after the above algorithm compensation is mostly over 85%, of which the maximum hysteresis corrected rate is 93.81% and the hysteresis error is less than 2.5%. Experiments prove the effectiveness of the above multi-stroke compensation algorithm based on the inverse model and extend the research on piezoelectric hysteresis characteristics to multi-stroke compensation.

Automated Raman based cell sorting with 3D microfluidics.

Raman activated cell sorting has emerged as a label-free technology that can link phenotypic function with genotypic properties of cells. However, its broad implementation is limited by challenges associated with throughput and the complexity of biological systems. Here, we describe a three-dimensional hydrodynamic focusing microfluidic system for a fully automated, continuous Raman activated cell sorting (3D-RACS). The system consists of a 3D printed detection chamber (1 mm3) that is integrated with a PDMS based sorting unit, optical sensors and an in-line collection module. It has the ability to precisely position cells in the detection chamber for Raman measurements, effectively eliminating spectroscopic interference from the device materials. This enables the sorting of a range of cell sizes (from 1 µm bacteria to 10's µm mammalian cells) with stable operation over >8 hours and high throughput. As a proof-of-concept demonstration, Raman-activated sorting of mixtures of Chlorella vulgaris and E. coli has demonstrated a purity level of 92.0% at a throughput of 310 cells per min. The platform employed in this demonstration features a simple "Raman window" detection system, enabling it to be built on a standard, inverted microscope. Together with its facile and robust operation, it provides a versatile tool for function-based flow cytometry and sorting applications in the fields of microbiology, biotechnology, life science and diagnostics.

Chronic lead exposure induces histopathological damage, microbiota dysbiosis and immune disorder in the cecum of female Japanese quails (Coturnix japonica).

Lead (Pb) is one of the most hazardous metals to human and wildlife and it also has multiple negative impacts on birds. However, its influences on bird gut morphology and intestinal microbiota were still unclear. We used female Japanese quails (Coturnix japonica) to examine the effects of chronic lead exposure (0, 50 ppm and 1000 ppm) on cecal histology, microbial communities and immune function. The results showed 50 ppm lead exposure caused subtle damages of cecum cell structure. However, 1000 ppm lead exposure caused severe cecum histopathological changes characterized by mucosa abscission, Lieberkühn glands destruction and lymphocyte proliferation. Moreover, both lead concentrations induced ultrastructural damages featured by nucleus pyknosis, mitochondrial vacuolation and microvilli contraction. Meanwhile, microbial community structure, species diversity, taxonomic compositions and taxa abundance in the cecum were affected by lead exposure. Furthermore, the mRNA relative expression of immunity-related genes such as interleukin 2 (IL-2) and gamma interferon (IFN-γ) was significantly downregulated while that of interleukin 6 (IL-6), tumor necrosis factor α (TNF-α) and natural killer kappa B (NF-κB) was significantly upregulated in the cecum of 50 and 1000 ppm lead exposure groups. We concluded that lead exposure may cause gut health impairment of female Japanese quails by inducing cecal histopathological changes, microbiota dysbiosis and cecal immune disorder.

Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network.

Heart disease is a malignant threat to human health. Electrocardiogram (ECG) tests are used to help diagnose heart disease by recording the heart's activity. However, automated medical-aided diagnosis with computers usually requires a large volume of labeled clinical data without patients' privacy to train the model, which is an empirical problem that still needs to be solved. To address this problem, we propose a generative adversarial network (GAN), which is composed of a bidirectional long short-term memory(LSTM) and convolutional neural network(CNN), referred as BiLSTM-CNN,to generate synthetic ECG data that agree with existing clinical data so that the features of patients with heart disease can be retained. The model includes a generator and a discriminator, where the generator employs the two layers of the BiLSTM networks and the discriminator is based on convolutional neural networks. The 48 ECG records from individuals of the MIT-BIH database were used to train the model. We compared the performance of our model with two other generative models, the recurrent neural network autoencoder(RNN-AE) and the recurrent neural network variational autoencoder (RNN-VAE). The results showed that the loss function of our model converged to zero the fastest. We also evaluated the loss of the discriminator of GANs with different combinations of generator and discriminator. The results indicated that BiLSTM-CNN GAN could generate ECG data with high morphological similarity to real ECG recordings.

pH-Induced Switchable Superwettability of Efficient Antibacterial Fabrics for Durable Selective Oil/Water Separation.

The superhydrophobic antibacterial fabrics with intelligent switchable wettability were fabricated by the cross-link reaction among pH-responsive antibacterial copolymer tethered hydroxyl groups, methylol-contained poly(ureaformaldehyde) nanoparticles (PUF NPs), and hexamethylene diisocyanate. It was found that the surface concentration of N+ were heavily influenced by acid solutions, resulting in the rapid wettability conversion from superhydrophobicity/superoleophilicity to superhydrophilicity/underwater superoleophobicity in a remarkably short time. The above responsiveness feature of coated cotton fabric contributes a prominent selective oil/water separation property, and the separation efficiency invariably remained at greater than 95% even after 20 reuse cycles, which exhibited brilliant durability. More importantly, the coated cotton fabric possessed excellent self-cleaning performance after contamination by oil and held high bactericidal rate (more than 80%) regardless of pH treatment, and thus could abate the surface biological pollution caused by bacteria proliferation. The attractive properties of the prepared smart superwetting materials shows great promise for potential application in oil/water separation from an environmental-protection perspective.

A collaborative recommend algorithm based on bipartite community.

The recommendation algorithm based on bipartite network is superior to traditional methods on accuracy and diversity, which proves that considering the network topology of recommendation systems could help us to improve recommendation results. However, existing algorithms mainly focus on the overall topology structure and those local characteristics could also play an important role in collaborative recommend processing. Therefore, on account of data characteristics and application requirements of collaborative recommend systems, we proposed a link community partitioning algorithm based on the label propagation and a collaborative recommendation algorithm based on the bipartite community. Then we designed numerical experiments to verify the algorithm validity under benchmark and real database.

Unregistered biological words recognition by Q-learning with transfer learning.

Unregistered biological words recognition is the process of identification of terms that is out of vocabulary. Although many approaches have been developed, the performance approaches are not satisfactory. As the identification process can be viewed as a Markov process, we put forward a Q-learning with transfer learning algorithm to detect unregistered biological words from texts. With the Q-learning, the recognizer can attain the optimal solution of identification during the interaction with the texts and contexts. During the processing, a transfer learning approach is utilized to fully take advantage of the knowledge gained in a source task to speed up learning in a different but related target task. A mapping, required by many transfer learning, which relates features from the source task to the target task, is carried on automatically under the reinforcement learning framework. We examined the performance of three approaches with GENIA corpus and JNLPBA04 data. The proposed approach improved performance in both experiments. The precision, recall rate, and F score results of our approach surpassed those of conventional unregistered word recognizer as well as those of Q-learning approach without transfer learning.

A reward optimization method based on action subrewards in hierarchical reinforcement learning.

Reinforcement learning (RL) is one kind of interactive learning methods. Its main characteristics are "trial and error" and "related reward." A hierarchical reinforcement learning method based on action subrewards is proposed to solve the problem of "curse of dimensionality," which means that the states space will grow exponentially in the number of features and low convergence speed. The method can reduce state spaces greatly and choose actions with favorable purpose and efficiency so as to optimize reward function and enhance convergence speed. Apply it to the online learning in Tetris game, and the experiment result shows that the convergence speed of this algorithm can be enhanced evidently based on the new method which combines hierarchical reinforcement learning algorithm and action subrewards. The "curse of dimensionality" problem is also solved to a certain extent with hierarchical method. All the performance with different parameters is compared and analyzed as well.

Segmentation of neuronal structures using SARSA (λ)-based boundary amendment with reinforced gradient-descent curve shape fitting.

The segmentation of structures in electron microscopy (EM) images is very important for neurobiological research. The low resolution neuronal EM images contain noise and generally few features are available for segmentation, therefore application of the conventional approaches to identify the neuron structure from EM images is not successful. We therefore present a multi-scale fused structure boundary detection algorithm in this study. In the algorithm, we generate an EM image Gaussian pyramid first, then at each level of the pyramid, we utilize Laplacian of Gaussian function (LoG) to attain structure boundary, we finally assemble the detected boundaries by using fusion algorithm to attain a combined neuron structure image. Since the obtained neuron structures usually have gaps, we put forward a reinforcement learning-based boundary amendment method to connect the gaps in the detected boundaries. We use a SARSA (λ)-based curve traveling and amendment approach derived from reinforcement learning to repair the incomplete curves. Using this algorithm, a moving point starts from one end of the incomplete curve and walks through the image where the decisions are supervised by the approximated curve model, with the aim of minimizing the connection cost until the gap is closed. Our approach provided stable and efficient structure segmentation. The test results using 30 EM images from ISBI 2012 indicated that both of our approaches, i.e., with or without boundary amendment, performed better than six conventional boundary detection approaches. In particular, after amendment, the Rand error and warping error, which are the most important performance measurements during structure segmentation, were reduced to very low values. The comparison with the benchmark method of ISBI 2012 and the recent developed methods also indicates that our method performs better for the accurate identification of substructures in EM images and therefore useful for the identification of imaging features related to brain diseases.